NDLI: Image colourisation using linear neighbourhood propagation and weighted smoothing

Content Provider	IET Digital Library
Author	Chen, Ying Zong, Gaigai Cao, Guangcheng Dong, Jiawei
Abstract	Image colourisation is the process used to add colour to a grey-level image using computers and is based on the local artificial colours of the image. This study proposes a colourisation method based on linear neighbourhood propagation and weighted smoothing. The method avoids the colour-crossing problem associated with existing methods and enables the achievement of natural and smooth colours. The image is initially coloured by hands, and this is followed by the development of a linear neighbourhood matrix model. The process is completed by global colourisation using the priority method. The final colourised image emerges through a filter-based smoothing treatment from the boundary to the smooth area. Compared to currently popular colourisation methods, the proposed method simplifies the requirements for the initial colourisation and also produces clearer, natural, and higher-quality images.
Starting Page	285
Ending Page	291
Page Count	7
ISSN	17519659
Volume Number	11
e-ISSN	17519667
Issue Number	Issue 5, Apr (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/11/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2016.0371
Journal	IET Image Processing
Publisher Date	2017-01-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Clearer Image Computer Vision And Image Processing Technique Filter-based Smoothing Treatment Global Colourisation Grey-level Image Higher-quality Image Image Colour Analysis Image Colourisation Linear Algebra Linear Neighbourhood Matrix Model Linear Neighbourhood Propagation Local Artificial Colours Matrix Algebra Natural Colours Natural Image Numerical Analysis Optical, Image And Video Signal Processing Priority Method Smooth Colours Smoothing Method Weighted Smoothing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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